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Record W4310202134 · doi:10.3390/w14233850

Numerical Study on Measures for Protecting the Go-Cong Coastlines (Vietnam) from Erosion

2022· article· en· W4310202134 on OpenAlexaff
Dinh Cong San, Nguyen Binh Duong, Nguyen Cong Phong, Tu Le Xuan, Damien Pham Van Bang, Sylvain Guillou, Kim Dan Nguyen

Bibliographic record

VenueWater · 2022
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicCoastal and Marine Dynamics
Canadian institutionsInstitut National de la Recherche Scientifique
Fundersnot available
KeywordsBreakwaterAccretion (finance)ShoalShoreSubmarine pipelineErosionCoastal erosionElevation (ballistics)GeologyHydrology (agriculture)StormEnvironmental scienceOceanographyGeomorphologyGeotechnical engineeringGeometryPhysics

Abstract

fetched live from OpenAlex

Every year, in the Vietnam Mekong Delta Coastal Zone (VMDCZ), erosions cause approximately 300 ha of agricultural land loss. Therefore, measures for shoreline protection are urgently needed. This paper discusses the impacts of protection measures in the Go-Cong Coastal Zone to prevent erosion/accretion processes, predicted by two numerical models, MIKE21-FM and TELEMAC-2D. Hard and soft measures have been proposed using breakwaters and sandbars, respectively. The simulations show that the erosion/accretion trends provided by both models are similar. For breakwaters, MIKE21-FM provides less accretion than TELEMAC-2D in areas extending over 300 m and 500 m from shorelines. However, for sandbars, MIKE21-FM shows higher accretion within areas extending over 500 m but less than 300 m. Sandbars cause higher accretion in a larger area, extending over 1000 m offshore. The simulation results allow us to propose two alternative measures: (1) a row of several breakwater units will be implanted at 300 m offshore. The length of each unit is 600 m, with a gap between two neighbouring units of 70 m and a crest elevation of 2.2 m above mean sea level (MSL). (2) A row of sandbar units will be posed at 500 m offshore, with a unit length of 1000 m and a gap between the two neighbouring units of 200 m. The crest elevation is fixed at MSL.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.262
Threshold uncertainty score0.745

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.026
GPT teacher head0.225
Teacher spread0.199 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations8
Published2022
Admission routes1
Has abstractyes

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